Multi-Agent System

Multi-Agent Support Automation

Personal Project

I designed a multi-agent support system that automates routine tickets while protecting customers from bad AI responses. The architecture uses confidence checks and human-in-the-loop routing to ensure quality at scale.

n8n Claude Sonnet 4 GPT-4 RAG Multi-Agent Architecture Human-in-the-Loop Executive Dashboard

74% Workload Reduced

Multi Agent System

The Problem

Support teams spend 60-70% of their time on repetitive questions while complex issues wait. The cost isn't just inefficiency - it's frustrated customers and burned-out agents.

Why Multi-Agent

Single-model solutions force a tradeoff between automation and quality. Multi-agent architecture lets each agent be tuned independently - triage for classification, resolution for response quality, and a separate quality gate to catch failures.

How It Works

Incoming ticket is analyzed by the Triage Agent to classify urgency and topic

Low-urgency tickets route to RAG Agent, which retrieves relevant knowledge base articles and drafts a response

Confidence check evaluates the AI response - uncertain answers get flagged for human review

High-urgency tickets route directly to humans with full context and suggested actions

Executive dashboard tracks resolution rates, escalation patterns, and AI confidence scores

Architecture Decisions

Component	Technology	Purpose
Orchestration	n8n	Workflow automation connecting all agents and routing logic
Triage Agent	Claude Sonnet 4	Classifies urgency, extracts intent, determines routing path
Quick Resolution Agent	GPT-4 + Vector DB	Handles straightforward queries with concise, direct responses
Complex Resolution Agent	GPT-4 + Vector DB	Handles technical/nuanced questions with detailed explanations
Confidence Check	n8n Rule-based Logic	Evaluates AI response quality before sending to customer
Human Routing	n8n	Escalates uncertain or urgent tickets with full context
Executive Dashboard	Looker Studio	Tracks metrics, patterns, and system health

Human-in-the-Loop by Design: The system never sends uncertain responses to customers. When the confidence check detects short responses or explicit uncertainty ("I don't have that information"), it routes to human review with the AI's draft response.

Why Claude for Triage, GPT-4 for Resolution? Claude excels at structured classification. GPT-4's native file search made RAG faster to implement. Right model for each task beats one-size-fits-all.

What I Learned

💡 Quality gates prevent bad responses. Every AI response gets checked before reaching customers. Suspiciously short answers or uncertainty phrases ("I don't have that information") automatically route to human review.
💡 Escalation with context. Human support teams see classification, urgency, and sentiment upfront. They know immediately if it's an angry billing dispute or technical question.
💡 Multi-LLM beats single model. Claude for classification, GPT-4 for resolution. Each excels at its task. Specialized components beat one-size-fits-all.